Senior Knowledge Graph Engineer

EcoVadis

15h•Hybrid

About The Position

EcoVadis is seeking a hands-on, production-focused Senior Knowledge Graph Engineer to join their AI Center of Excellence. This role is responsible for leveraging AI and machine learning to drive innovation. The engineer will operationalize domain ontologies into high-throughput, multi-hop systems to power autonomous AI agents that address complex sustainability challenges such as decarbonisation, sustainable procurement compliance, and supply chain resilience. The position involves bridging the gap between unstructured sustainability disclosures and structured graph databases, and building entity-resolution pipelines to prepare enterprise data for agent use.

Requirements

Degree in Computer Science, Mathematics, Engineering, or a related technical discipline
4+ years of production experience building and querying graph databases, specifically Labeled Property Graphs (Neo4j, Memgraph, TigerGraph) or RDF Triple Stores (GraphDB, Stardog, Virtuoso)
Strong experience in cloud technology, preferably Azure and its ecosystem (e.g., Azure Foundry, Azure Bicep, AzureML and Azure Cloud Storage)
Advanced proficiency in Python (RDFLib, NetworkX, PyGraphistry) for building scalable, production-grade data pipelines
Experience building entity extraction pipelines using modern NLP frameworks (LangChain, LlamaIndex, spaCy) or LLM-based structured extraction
Hands-on experience with modern data transformation tools (dbt) and integrating graph databases with vector stores (Qdrant, Pinecone, pgvector) for hybrid search architectures
Solid understanding of semantic web standards (RDF, RDFS and OWL, SKOS, SHACL, RDF-star, SPARQL), graph schema design principles (T-Box vs. A-Box separation), and mapping languages for dealing with heterogeneous data structures (RML, R2RML)

Nice To Haves

Experience working with domain-specific supply chain, carbon accounting (GHG Protocol), or lifecycle assessment (LCA) data structures is a plus
Direct experience building Model Context Protocol (MCP) servers to expose graph tools to LLM agents is a plus
Experience with enterprise OBDA approaches at-scale is a plus

Responsibilities

Design, implement, and maintain high-speed GraphRAG ingestion pipelines that transform relational data (ERP, SQL), unstructured ESG reports, and streaming feeds into operational Labeled Property Graphs (Neo4j, Memgraph) and RDF Triple Stores.
Build automated Named Entity Recognition (NER), entity linking, and deduplication workflows to resolve mismatched vendor profiles, material SKUs, and facility coordinates into unified canonical graph nodes.
Implement automated ETL/ELT pipelines to map and federate internal supply chain data with external, open-source ontologies and registries (such as GLEIF for corporate ownership, W3C SSN/SOSA for IoT sensors, and Copernicus for geo-hazard alerts, PROV-O for data provenance).
Partner with AI/ML Engineers to build low-latency GraphRAG retrieval layers—writing optimized Cypher and SPARQL queries, implementing NL2Query tools for agents, hybrid vector-graph indexing pipelines, and Model Context Protocol (MCP) tool endpoints for autonomous LLM agents.
Operationalize SHACL (Shapes Constraint Language) shapes into automated data quality tests within CI/CD pipelines to prevent hallucinated or non-compliant data mutations from entering the enterprise knowledge graph.
Optimize multi-hop query performance, graph partitioning, and database indexing strategies to handle sub-second traversal over billions of nodes and edges.